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---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-cord_100
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# layoutlmv3-finetuned-cord_100
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3036
- Precision: 0.9149
- Recall: 0.9309
- F1: 0.9228
- Accuracy: 0.9419
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 4.17 | 250 | 0.6391 | 0.8080 | 0.8093 | 0.8087 | 0.8312 |
| 0.9327 | 8.33 | 500 | 0.3636 | 0.8790 | 0.8891 | 0.8840 | 0.9088 |
| 0.9327 | 12.5 | 750 | 0.3144 | 0.9001 | 0.9103 | 0.9052 | 0.9288 |
| 0.1743 | 16.67 | 1000 | 0.2957 | 0.9102 | 0.9240 | 0.9170 | 0.9360 |
| 0.1743 | 20.83 | 1250 | 0.2963 | 0.9109 | 0.9248 | 0.9178 | 0.9334 |
| 0.0551 | 25.0 | 1500 | 0.2943 | 0.9207 | 0.9263 | 0.9235 | 0.9411 |
| 0.0551 | 29.17 | 1750 | 0.3034 | 0.9145 | 0.9263 | 0.9203 | 0.9360 |
| 0.0249 | 33.33 | 2000 | 0.3059 | 0.9162 | 0.9301 | 0.9231 | 0.9394 |
| 0.0249 | 37.5 | 2250 | 0.3019 | 0.9147 | 0.9293 | 0.9220 | 0.9385 |
| 0.0153 | 41.67 | 2500 | 0.3036 | 0.9149 | 0.9309 | 0.9228 | 0.9419 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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